Integration of genetics into a systems model of electrocardiographic traits using humanCVD BeadChip

<p>Background—Electrocardiographic traits are important, substantially heritable determinants of risk of arrhythmias and sudden cardiac death.</p> <p>Methods and Results—In this study, 3 population-based cohorts (n=10 526) genotyped with the Illumina HumanCVD Beadchip and 4 quantitative electrocardiographic traits (PR interval, QRS axis, QRS duration, and QTc interval) were evaluated for single-nucleotide polymorphism associations. Six gene regions contained single nucleotide polymorphisms associated with these traits at P<10−6, including SCN5A (PR interval and QRS duration), CAV1-CAV2 locus (PR interval), CDKN1A (QRS duration), NOS1AP, KCNH2, and KCNQ1 (QTc interval). Expression quantitative trait loci analyses of top associated single-nucleotide polymorphisms were undertaken in human heart and aortic tissues. NOS1AP, SCN5A, IGFBP3, CYP2C9, and CAV1 showed evidence of differential allelic expression. We modeled the effects of ion channel activity on electrocardiographic parameters, estimating the change in gene expression that would account for our observed associations, thus relating epidemiological observations and expression quantitative trait loci data to a systems model of the ECG.</p> <p>Conclusions—These association results replicate and refine the mapping of previous genome-wide association study findings for electrocardiographic traits, while the expression analysis and modeling approaches offer supporting evidence for a functional role of some of these loci in cardiac excitation/conduction.</p&gt

Early life risk factors and their cumulative effects as predictors of overweight in Spanish children

Objectives: To explore early life risk factors of overweight/obesity at age 6 years and their cumulative effects on overweight/obesity at ages 2, 4 and 6 years. Methods: Altogether 1031 Spanish children were evaluated at birth and during a 6-year follow-up. Early life risk factors included: parental overweight/obesity, parental origin/ethnicity, maternal smoking during pregnancy, gestational weight gain, gestational age, birth weight, caesarean section, breastfeeding practices and rapid infant weight gain collected via hospital records. Cumulative effects were assessed by adding up those early risk factors that significantly increased the risk of overweight/obesity. We conducted binary logistic regression models. Results: Rapid infant weight gain (OR 2.29, 99% CI 1.54–3.42), maternal overweight/obesity (OR 1.93, 99% CI 1.27–2.92), paternal overweight/obesity (OR 2.17, 99% CI 1.44–3.28), Latin American/Roma origin (OR 3.20, 99% CI 1.60–6.39) and smoking during pregnancy (OR 1.61, 99% CI 1.01–2.59) remained significant after adjusting for confounders. A higher number of early life risk factors accumulated was associated with overweight/obesity at age 6 years but not at age 2 and 4 years. Conclusions: Rapid infant weight gain, parental overweight/obesity, maternal smoking and origin/ethnicity predict childhood overweight/obesity and present cumulative effects. Monitoring children with rapid weight gain and supporting a healthy parental weight are important for childhood obesity prevention

Maternal occupation during pregnancy, birth weight, and length of gestation: Combined analysis of 13 European birth cohorts

Objectives We assessed whether maternal employment during pregnancy – overall and in selected occupational sectors – is associated with birth weight, small for gestational age (SGA), term low birth weight (LBW), length of gestation, and preterm delivery in a population-based birth cohort design. Methods We used data from >200 000 mother-child pairs enrolled in 13 European birth cohorts and compared employed versus non-employed women. Among employees, we defined groups of occupations representing the main sectors of employment for women where potential reproductive hazards are considered to be present. The comparison group comprised all other employed women not included in the occupational sector being assessed. We performed meta-analyses of cohort-specific estimates and explored heterogeneity. Results Employees had a lower risk of preterm delivery than non-employees [adjusted odds ratio (ORadj) 0.86, 95% confidence interval (95% CI) 0.81–0.91]. Working in most of the occupational sectors studied was not associated with adverse birth outcomes. Being employed as a nurse was associated with lower risk SGA infants (ORadj 0.91, 95% CI 0.84–0.99) whereas food industry workers had an increased risk of preterm delivery (ORadj 1.50, 95% CI 1.12–2.02). There was little evidence for heterogeneity between cohorts. Conclusions This study suggests that, overall, employment during pregnancy is associated with a reduction in the risk of preterm birth and that work in certain occupations may affect pregnancy outcomes. This exploratory study provides an important platform on which to base further prospective studies focused on the potential consequences of maternal occupational exposures during pregnancy on child development

The genetics of the mood disorder spectrum:genome-wide association analyses of over 185,000 cases and 439,000 controls

Background Mood disorders (including major depressive disorder and bipolar disorder) affect 10-20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Despite their diagnostic distinction, multiple approaches have shown considerable sharing of risk factors across the mood disorders. Methods To clarify their shared molecular genetic basis, and to highlight disorder-specific associations, we meta-analysed data from the latest Psychiatric Genomics Consortium (PGC) genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; non-overlapping N = 609,424). Results Seventy-three loci reached genome-wide significance in the meta-analysis, including 15 that are novel for mood disorders. More genome-wide significant loci from the PGC analysis of major depression than bipolar disorder reached genome-wide significance. Genetic correlations revealed that type 2 bipolar disorder correlates strongly with recurrent and single episode major depressive disorder. Systems biology analyses highlight both similarities and differences between the mood disorders, particularly in the mouse brain cell-types implicated by the expression patterns of associated genes. The mood disorders also differ in their genetic correlation with educational attainment – positive in bipolar disorder but negative in major depressive disorder. Conclusions The mood disorders share several genetic associations, and can be combined effectively to increase variant discovery. However, we demonstrate several differences between these disorders. Analysing subtypes of major depressive disorder and bipolar disorder provides evidence for a genetic mood disorders spectrum